Analyzing a continuous flow of data continues to be a challenging task. Computations have to be performed at the same speed as the continuous flow of data is received by a computing system. Batch processing is not an option. Examples of such continuously flowing data are data from social media and video streams from, e.g., a surveillance camera. One approach to cope with these data streams is stream processing using graphs. A successful approach distributes the graph over a cluster of nodes, i.e., computing nodes. The nodes may be connected in series and/or in parallel. Each of the nodes may perform a specific task on the data. For a management of these interconnected nodes in the graph, a graph management system, as well as, a related framework for coordinating the work of all the nodes may be used.
In the past, a lot of emphasis has been put on the design of the graph as well as on the individual notes. Little or no emphasis was given to the requirements for the connection technology between the individual computing nodes of the graph.